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Showing papers in "IEEE Transactions on Instrumentation and Measurement in 2012"


Journal ArticleDOI
TL;DR: A new method to accurately locate persons indoors by fusing inertial navigation system (INS) techniques with active RFID technology, using the residuals between the INS-predicted reader-to-tag ranges and the ranges derived from a generic RSS path-loss model.
Abstract: We present a new method to accurately locate persons indoors by fusing inertial navigation system (INS) techniques with active RFID technology. A foot-mounted inertial measuring units (IMUs)-based position estimation method, is aided by the received signal strengths (RSSs) obtained from several active RFID tags placed at known locations in a building. In contrast to other authors that integrate IMUs and RSS with a loose Kalman filter (KF)-based coupling (by using the residuals of inertial- and RSS-calculated positions), we present a tight KF-based INS/RFID integration, using the residuals between the INS-predicted reader-to-tag ranges and the ranges derived from a generic RSS path-loss model. Our approach also includes other drift reduction methods such as zero velocity updates (ZUPTs) at foot stance detections, zero angular-rate updates (ZARUs) when the user is motionless, and heading corrections using magnetometers. A complementary extended Kalman filter (EKF), throughout its 15-element error state vector, compensates the position, velocity and attitude errors of the INS solution, as well as IMU biases. This methodology is valid for any kind of motion (forward, lateral or backward walk, at different speeds), and does not require an offline calibration for the user gait. The integrated INS+RFID methodology eliminates the typical drift of IMU-alone solutions (approximately 1% of the total traveled distance), resulting in typical positioning errors along the walking path (no matter its length) of approximately 1.5 m.

435 citations


Journal ArticleDOI
TL;DR: A microwave method based on complementary split-ring resonators (CSRRs) is proposed for dielectric characterization of planar materials and eliminates the extensive sample preparation procedure needed in resonance-based methods.
Abstract: A microwave method based on complementary split-ring resonators (CSRRs) is proposed for dielectric characterization of planar materials. The technique presents advantages such as high measurement sensitivity and eliminates the extensive sample preparation procedure needed in resonance-based methods. A sensor in the shape of CSRRs working at a 0.8-1.3 GHz band is demonstrated. The sensor is etched in the ground plane of a microstrip line to effectively create a stopband filter. The frequencies at which minimum transmission and minimum reflection are observed depend on the permittivity of the sample under test. The minimum transmission frequency shifts from 1.3 to 0.8 GHz as the sample permittivity changes from 1 to 10. The structure is fabricated using printed circuit board technology. Numerical findings are experimentally verified.

341 citations


Journal ArticleDOI
TL;DR: The merit of the method is clearly demonstrated using convergence and correlation analysis, thus making it best suitable for present-day pulse oximeters utilizing PPG sensor head with a single pair of source and detector, which does not have any extra hardware meant for capturing noise reference signal.
Abstract: The performance of pulse oximeters is highly influenced by motion artifacts (MAs) in photoplethysmographic (PPG) signals. In this paper, we propose a simple and efficient approach based on adaptive step-size least mean squares (AS-LMS) adaptive filter for reducing MA in corrupted PPG signals. The presented method is an extension to our prior work on efficient use of adaptive filters for reduction of MA in PPG signals. The novelty of the method lies in the fact that a synthetic noise reference signal for an adaptive filtering process, representing MA noise, is generated internally from the MA-corrupted PPG signal itself instead of using any additional hardware such as accelerometer or source-detector pair for acquiring noise reference signal. Thus, the generated noise reference signal is then applied to the AS-LMS adaptive filter for artifact removal. While experimental results proved the efficacy of the proposed scheme, the merit of the method is clearly demonstrated using convergence and correlation analysis, thus making it best suitable for present-day pulse oximeters utilizing PPG sensor head with a single pair of source and detector, which does not have any extra hardware meant for capturing noise reference signal. In addition to arterial oxygen saturation estimation, the artifact reduction method facilitated the waveform contour analysis on artifact-reduced PPG, and the conventional parameters were evaluated for assessing the arterial stiffness.

308 citations


Journal ArticleDOI
TL;DR: An automatic sleep-scoring method combining multiscale entropy (MSE) and autoregressive (AR) models for single-channel EEG and to assess the performance of the method comparatively with manual scoring based on full polysomnograms is proposed.
Abstract: In this paper, we propose an automatic sleep-scoring method combining multiscale entropy (MSE) and autoregressive (AR) models for single-channel EEG and to assess the performance of the method comparatively with manual scoring based on full polysomnograms. This is the first time that MSE has ever been applied to sleep scoring. All-night polysomnograms from 20 healthy individuals were scored using the Rechtschaffen and Kales rules. The developed method analyzed the EEG signals of C3-A2 for sleep staging. The results of automatic and manual scorings were compared on an epoch-by-epoch basis. A total of 8480 30-s sleep EEG epochs were measured and used for performance evaluation. The epoch-by-epoch comparison was made by classifying the EEG epochs into five states (Wake/REM/S1/S2/SWS) by the proposed method and manual scoring. The overall sensitivity and kappa coefficient of MSE alone are 76.9% and 0.65, respectively. Moreover, the overall sensitivity and kappa coefficient of our proposed method of integrating MSE, AR models, and a smoothing process can reach the sensitivity level of 88.1% and 0.81, respectively. Our results show that MSE is a useful and representative feature for sleep staging. It has high accuracy and good home-care applicability because a single EEG channel is used for sleep staging.

273 citations


Journal ArticleDOI
TL;DR: A real-time visual inspection system (VIS) for discrete surface defects that is very fast with a linear computational time complexity, and it can be in real time to run on a 216-km/h test train under the experimental setup.
Abstract: Discrete surface defects impact the riding quality and safety of a railway system. However, it is a challenge to inspect such defects in a vision system because of illumination inequality and the variation of reflection property of rail surfaces. This paper puts forward a real-time visual inspection system (VIS) for discrete surface defects. VIS first acquires a rail image by the image acquisition system, and then, it cuts the subimage of rail track by the track extraction algorithm. Subsequently, VIS enhances the contrast of the rail image using the local normalization (LN) method, which is nonlinear and illumination independent. At last, VIS detects defects using the defect localization based on projection profile (DLBP), which is robust to noise and very fast. Our experimental results demonstrate that VIS detects the Type-II defects with a recall of 93.10% and Type-I defects with a recall of 80.41%, and the proposed LN method and DLBP algorithm are better than the related well-established approaches. Furthermore, VIS is very fast with a linear computational time complexity, and it can be in real time to run on a 216-km/h test train under our experimental setup.

235 citations


Journal ArticleDOI
TL;DR: Experimental results show that, compared with raw data transmission, on-sensor fault diagnosis could reduce payload transmission data by 99%, decrease node energy consumption by 97%, and prolong node lifetime from 106 to 150 h, an increase of 43%.
Abstract: This paper proposes a novel industrial wireless sensor network (IWSN) for industrial machine condition monitoring and fault diagnosis. In this paper, the induction motor is taken as an example of monitored industrial equipment due to its wide use in industrial processes. Motor stator current and vibration signals are measured for further processing and analysis. On-sensor node feature extraction and on-sensor fault diagnosis using neural networks are then investigated to address the tension between the higher system requirements of IWSNs and the resource-constrained characteristics of sensor nodes. A two-step classifier fusion approach using Dempster-Shafer theory is also explored to increase diagnosis result quality. Four motor operating conditions-normal without load, normal with load, loose feet, and mass imbalance-are monitored to evaluate the proposed system. Experimental results show that, compared with raw data transmission, on-sensor fault diagnosis could reduce payload transmission data by 99%, decrease node energy consumption by 97%, and prolong node lifetime from 106 to 150 h, an increase of 43%. The final fault diagnosis results using the proposed classifier fusion approach give a result certainty of at least 97.5%. To leverage the advantages of on-sensor fault diagnosis, another system operating mode is explored, which only transmits the fault diagnosis result when a fault happens or at a fixed interval. For this mode, the node lifetime reaches 73 days if sensor nodes transmit diagnosis results once per hour.

211 citations


Journal ArticleDOI
TL;DR: Experimental tests were conducted to verify the performance of the proposed algorithm in various dynamic condition settings and to provide further insight into the variations in the estimation accuracy; two different approaches for dealing with the estimation problem during dynamic conditions were compared.
Abstract: This paper proposes a Kalman filter-based attitude (ie, roll and pitch) estimation algorithm using an inertial sensor composed of a triaxial accelerometer and a triaxial gyroscope In particular, the proposed algorithm has been developed for accurate attitude estimation during dynamic conditions, in which external acceleration is present Although external acceleration is the main source of the attitude estimation error and despite the need for its accurate estimation in many applications, this problem that can be critical for the attitude estimation has not been addressed explicitly in the literature Accordingly, this paper addresses the combined estimation problem of the attitude and external acceleration Experimental tests were conducted to verify the performance of the proposed algorithm in various dynamic condition settings and to provide further insight into the variations in the estimation accuracy Furthermore, two different approaches for dealing with the estimation problem during dynamic conditions were compared, ie, threshold-based switching approach versus acceleration model-based approach Based on an external acceleration model, the proposed algorithm was capable of estimating accurate attitudes and external accelerations for short accelerated periods, showing its high effectiveness during short-term fast dynamic conditions Contrariwise, when the testing condition involved prolonged high external accelerations, the proposed algorithm exhibited gradually increasing errors However, as soon as the condition returned to static or quasi-static conditions, the algorithm was able to stabilize the estimation error, regaining its high estimation accuracy

180 citations


Journal ArticleDOI
TL;DR: A methodology for rotational machine health monitoring and fault detection using empirical mode decomposition (EMD)-based AE feature quantification is presented and incorporates a threshold-based denoising technique into EMD to increase the signal-to-noise ratio of the AE bursts.
Abstract: Acoustic emission (AE)-signal-based techniques have recently been attracting researchers' attention to rotational machine health monitoring and diagnostics due to the advantages of the AE signals over the extensively used vibration signals. Unlike vibration-based methods, the AE-based techniques are in their infant stage of development. From the perspective of machine health monitoring and fault detection, developing an AE-based methodology is important. In this paper, a methodology for rotational machine health monitoring and fault detection using empirical mode decomposition (EMD)-based AE feature quantification is presented. The methodology incorporates a threshold-based denoising technique into EMD to increase the signal-to-noise ratio of the AE bursts. Multiple features are extracted from the denoised signals and then fused into a single compressed AE feature. The compressed AE features are then used for fault detection based on a statistical method. A gear fault detection case study is conducted on a notional split-torque gearbox using AE signals to demonstrate the effectiveness of the methodology. A fault detection performance comparison using the compressed AE features with the existing EMD-based AE features reported in the literature is also conducted.

177 citations


Journal ArticleDOI
TL;DR: A foot motion filtering algorithm is presented for estimating foot kinematics relative to an earth-fixed reference frame during normal walking motion that incorporates novel methods for orientation estimation, gait detection, and position estimation.
Abstract: A foot motion filtering algorithm is presented for estimating foot kinematics relative to an earth-fixed reference frame during normal walking motion. Algorithm input data are obtained from a foot-mounted inertial/magnetic measurement unit. The sensor unit contains a three-axis accelerometer, a three-axis angular rate sensor, and a three-axis magnetometer. The algorithm outputs are the foot kinematic parameters, which include foot orientation, position, velocity, acceleration, and gait phase. The foot motion filtering algorithm incorporates novel methods for orientation estimation, gait detection, and position estimation. Accurate foot orientation estimates are obtained during both static and dynamic motion using an adaptive-gain complementary filter. Reliable gait detection is accomplished using a simple finite state machine that transitions between states based on angular rate measurements. Accurate position estimates are obtained by integrating acceleration data, which has been corrected for drift using zero velocity updates. Algorithm performance is examined using both simulations and real-world experiments. The simulations include a simple but effective model of the human gait cycle. The simulation and experimental results indicate that a position estimation error of less than 1% of the total distance traveled is achievable using commonly available commercial sensor modules.

162 citations


Journal ArticleDOI
TL;DR: An in-depth analysis of the effect of both steady-state and dynamic disturbances on single-cycle and multicycle windowed discrete Fourier transform (DFT)-based synchrophasor estimators and a new two-term window minimizing the detrimental effects of image frequency tone is proposed.
Abstract: Synchrophasor estimation accuracy is a well-known critical issue in systems for smart grid monitoring and control. This paper deals with an in-depth analysis of the effect of both steady-state and dynamic disturbances on single-cycle and multicycle windowed discrete Fourier transform (DFT)-based synchrophasor estimators. Unlike other qualitative or simulation-based results found in the literature, this work provides two accurate and easy-to-use analytical expressions that can be used to determine the worst case range of variation of the total vector error (TVE) due to off-nominal frequency deviations. In such conditions, estimation accuracy is limited by two factors, i.e., the infiltration caused by the input signal image frequency and the scalloping loss associated with the spectrum main lobe of the chosen window. Starting from the aforementioned general analysis, a new two-term window minimizing the detrimental effects of image frequency tone is proposed. The accuracy of the related DFT-based synchrophasor estimator is evaluated under both static and dynamic conditions, which is the most interesting scenario for future smart grids. Moreover, the effect of waveform frequency measurement uncertainty on scalloping loss compensation is quantified. Several simulation results (including the effects of noise, harmonic distortion, and amplitude and phase modulation) confirm that the proposed window can significantly improve the accuracy achievable with a simple single-cycle DFT estimator. Indeed, TVE values much smaller than 1% can be achieved even in the worst case conditions reported in the standard IEEE C37.118.1-2011, when the frequency waveform deviations are within ±4% of the nominal value. In addition, the proposed solution could be useful to improve the performance of more complex dynamic phasor estimators, e.g., those in which the first- and second-order terms of the phasor Taylor series expansion result from the differences of consecutive DFT-based phasor estimates.

130 citations


Journal ArticleDOI
Jianbo Yu1
TL;DR: A hidden Markov model (HMM) and contribution-analysis-based method to assess the machine health degradation and a novel machine health assessment indication, HMM-based Mahalanobis distance is proposed to provide a comprehensible indication for quantifying machine health states.
Abstract: Degradation parameter from normal to failure condition of machine part or system is needed as an object of health monitoring in condition-based maintenance (CBM). This paper proposes a hidden Markov model (HMM) and contribution-analysis-based method to assess the machine health degradation. A dynamic principal component analysis (DPCA) is used to extract effective features from vibration signals, where inherent signal autocorrelation is considered. A novel machine health assessment indication, HMM-based Mahalanobis distance is proposed to provide a comprehensible indication for quantifying machine health states. A variable-replacing-based contribution analysis method is developed to discover the effective features that are responsible for the detection and assessment of machine health degradation in its whole life. The experimental results based on a bearing test bed show the plausibility and effectiveness of the proposed methods, which can be considered as the machine health degradation monitoring model.

Journal ArticleDOI
TL;DR: The proposed method makes use of the full second-order information within three-phase signals, thus promising enhanced and robust frequency estimation and is well matched to unbalanced system conditions and also provides unbiased frequency estimation.
Abstract: A novel technique for online estimation of the fundamental frequency of unbalanced three-phase power systems is proposed. Based on Clarke's transformation and widely linear complex domain modeling, the proposed method makes use of the full second-order information within three-phase signals, thus promising enhanced and robust frequency estimation. The structure, mathematical formulation, and theoretical stability and statistical performance analysis of the proposed technique illustrate that, in contrast to conventional linear adaptive estimators, the proposed method is well matched to unbalanced system conditions and also provides unbiased frequency estimation. The proposed method is also less sensitive to the variations of the three-phase voltage amplitudes over time and in the presence of higher order harmonics. Simulations on both synthetic and real-world unbalanced power systems support the analysis.

Journal ArticleDOI
TL;DR: A mixed filtering approach is proposed for stable displacement estimation and waveband classification of the irregularities in the measured acceleration of railway tracks using acceleration data measured from high-speed trains.
Abstract: This paper describes a method of estimating irregularities in railway tracks using acceleration data measured from high-speed trains. Track irregularities are the main causes of the vibration of high-speed trains and thus should be carefully monitored to maintain the stability and ride quality of the trains. A mixed filtering approach is proposed for stable displacement estimation and waveband classification of the irregularities in the measured acceleration. Accelerometers are mounted on the axle box and the bogie of a high-speed train to measure the acceleration in the lateral and vertical directions. The estimated results are compared with those of a commercial track geometry measurement system. Finally, the performance of the proposed approach and the relationship between the mounted location of the accelerometers and the estimated track irregularities are discussed.

Journal ArticleDOI
TL;DR: A novel approach for machine health condition prognosis based on neuro-fuzzy systems (NFSs) and Bayesian algorithms and the results demonstrate that the proposed approach can predict machine conditions more accurately.
Abstract: This paper proposes a novel approach for machine health condition prognosis based on neuro-fuzzy systems (NFSs) and Bayesian algorithms. The NFS, after training with machine condition data, is employed as a prognostic model to forecast the evolution of the machine fault state with time. An online model update scheme is developed on the basis of the probability density function (PDF) of the NFS residuals between the actual and predicted condition data. Bayesian estimation algorithms adopt the model's predicted data as prior information in combination with online measurements to update the degree of belief in the forecasting estimations. In order to simplify the implementation of the proposed approach, a recursive Bayesian algorithm called particle filtering is utilized to calculate in real time a posterior PDF by a set of random samples (or particles) with associated weights. When new data become available, the weights of all particles are updated, and then, predictions are carried out, which form the PDF of the predicted estimations. The developed method is evaluated via two experimental cases-a cracked carrier plate and a faulty bearing. The prediction performance is compared with three prevalent machine condition predictors-recurrent neural networks, NFSs, and recurrent NFSs. The results demonstrate that the proposed approach can predict machine conditions more accurately.

Journal ArticleDOI
TL;DR: The impact of the phasor estimation models on the accuracy of these devices, focuses on algorithms proposed in the literature for the estimation of dynamic phasors, and studies their performances under several different conditions.
Abstract: Phasor measurement units (PMUs) are becoming one of the key issues of power network monitoring. They have to be able to perform accurate estimations of quantities of interest either under steady-state or transient conditions. Among all the sources which may contribute to the uncertainty introduced by PMUs, this paper analyzes the impact of the phasor estimation models on the accuracy of these devices, focuses on algorithms proposed in the literature for the estimation of dynamic phasors, and studies their performances under several different conditions.

Journal ArticleDOI
TL;DR: A robust indoor positioning system that provides 2-D positioning and orientation information for mobile objects and outperforms similar existing systems in minimizing the average positioning error is proposed.
Abstract: Ambient intelligence (AmI) considers responsive environments in which applications and services adapt their behavior according to the user's needs and changing context. One of the most challenging aspects for many applications in AmI environments is location and orientation of the surrounding objects. This is especially important for effective cooperation among mobile physical objects in such smart environments. In this paper, we propose a robust indoor positioning system that provides 2-D positioning and orientation information for mobile objects. The system utilizes low-range passive radio frequency identification (RFID) technology. The proposed system, which consists of RFID carpets and several peripherals for sensor data interpretation, is implemented and tested through extensive experiments. Our results show that the proposed system outperforms similar existing systems in minimizing the average positioning error.

Journal ArticleDOI
TL;DR: A novel frequency-shifting wavelet decomposition via the Hilbert transform is introduced for PQ analysis and can be used for estimating power quantities accurately and for detecting flickers.
Abstract: The wavelet transform, the S-transform, the Gabor transform, and the Wigner distribution function are popular techniques for power quality (PQ) analysis in electrical power systems. They are mainly used to identify power harmonics and power disturbances and to estimate power quantities in the presence of nonstationary power components such as root-mean-square values and total harmonic distortions. Recently, the Hilbert-Huang transform has been also used in PQ analysis. These techniques have proven to be useful in PQ analysis; however, their performances depend on the types of PQ events. In this paper, a novel frequency-shifting wavelet decomposition via the Hilbert transform is introduced for PQ analysis. The proposed algorithm overcomes the spectra leakage problem in the discrete wavelet packet transform and can be used for estimating power quantities accurately and for detecting flickers. The effectiveness of the proposed algorithm was verified by computer simulations and experimental tests.

Journal ArticleDOI
TL;DR: An approach for geometrically calibrating individual and multiple cameras in both the thermal and visible modalities is presented and a new geometric mask with high thermal contrast and not requiring a flood lamp is presented as an alternative calibration pattern.
Abstract: Accurate and efficient thermal-infrared (IR) camera calibration is important for advancing computer vision research within the thermal modality. This paper presents an approach for geometrically calibrating individual and multiple cameras in both the thermal and visible modalities. The proposed technique can be used to correct for lens distortion and to simultaneously reference both visible and thermal-IR cameras to a single coordinate frame. The most popular existing approach for the geometric calibration of thermal cameras uses a printed chessboard heated by a flood lamp and is comparatively inaccurate and difficult to execute. Additionally, software toolkits provided for calibration either are unsuitable for this task or require substantial manual intervention. A new geometric mask with high thermal contrast and not requiring a flood lamp is presented as an alternative calibration pattern. Calibration points on the pattern are then accurately located using a clustering-based algorithm which utilizes the maximally stable extremal region detector. This algorithm is integrated into an automatic end-to-end system for calibrating single or multiple cameras. The evaluation shows that using the proposed mask achieves a mean reprojection error up to 78% lower than that using a heated chessboard. The effectiveness of the approach is further demonstrated by using it to calibrate two multiple-camera multiple-modality setups. Source code and binaries for the developed software are provided on the project Web site.

Journal ArticleDOI
TL;DR: The proposed method is capable of estimating the accurate values of frequency, amplitude, and phase angle of the distorted current or voltage signals for a wide range of sampling frequency and measurement noise.
Abstract: Harmonic, which is becoming more and more important day by day in the emerging power system, is one of the most critical power quality parameters. In this paper, the estimation of signal parameters via rotational invariance technique (ESPRIT)-based method is proposed with an accurate model order (the number of frequency components) estimate for power system harmonic and interharmonics detection. It is demonstrated that, even for high noise signal, the proposed algorithm is able to accurately estimate the number of frequency components present in the signal. The proposed method is capable of estimating the accurate values of frequency, amplitude, and phase angle of the distorted current or voltage signals for a wide range of sampling frequency and measurement noise. The robustness of the proposed method has been tested on several simulated synthetic signals and measured experimental signals for different nonlinear loads.

Journal ArticleDOI
TL;DR: A recently developed descriptor, i.e., DAISY, is adapted to the problem to represent the salient features, and a dynamic scheme is developed to stochastically combine familial traits.
Abstract: Humans have the capability to recognize family members. Phrases such as “John has his father's nose” or “Joe has his mother's eyes” are quite common. Motivated by this, we consider the following question: Is it possible to develop a method to extract the salient familial traits in face images for kinship recognition? If this idea works, an instrument may be invented to measure familial relationships. This computational kinship measurement might have a large impact in real applications, such as child adoptions, trafficking/smuggling of children, and finding missing children. The novel problem is related to but very different from traditional face recognition. It is more challenging than a typical face recognition problem since we need to find subtle features that are reliable across a large span of ages (e.g., grandfather and grandson) and sex difference (e.g., mother and son). A recently developed descriptor, i.e., DAISY, is adapted to our problem to represent the salient features, and a dynamic scheme is developed to stochastically combine familial traits. Experiments are performed on a database to show that our new approach can perform reasonably well for kinship verification. The encouraging result may inspire further research on this emerging problem.

Journal ArticleDOI
TL;DR: A computing algorithm is proposed to define flame and fire edges clearly and continuously and the autoadaptive feature of the algorithm ensures that the primary symbolic flame/fire edges are identified for different scenarios.
Abstract: The determination of flame or fire edges is the process of identifying a boundary between the area where there is thermochemical reaction and those without. It is a precursor to image-based flame monitoring, early fire detection, fire evaluation, and the determination of flame and fire parameters. Several traditional edge-detection methods have been tested to identify flame edges, but the results achieved have been disappointing. Some research works related to flame and fire edge detection were reported for different applications; however, the methods do not emphasize the continuity and clarity of the flame and fire edges. A computing algorithm is thus proposed to define flame and fire edges clearly and continuously. The algorithm detects the coarse and superfluous edges in a flame/fire image first and then identifies the edges of the flame/fire and removes the irrelevant artifacts. The autoadaptive feature of the algorithm ensures that the primary symbolic flame/fire edges are identified for different scenarios. Experimental results for different flame images and video frames proved the effectiveness and robustness of the algorithm.

Journal ArticleDOI
TL;DR: A novel reference-free ultrasonic indoor location system that provides 3-D accuracy better than 9.5 cm in 99% of cases, an 80% accuracy improvement over the conventional AoA-only method.
Abstract: This paper presents a novel reference-free ultrasonic indoor location system. Unlike most previous proposals, the mobile device (MD) determines its own position based only on ultrasonic signals received at a compact sensor array and sent by a fixed independent beacon. No radio frequency or wired timing reference signal is used. Furthermore, the system is privacy aware and one way in that the receive-only MD determines its own position based on ultrasonic signals received from fixed transmit-only beacons. The MD uses a novel hybrid angle of arrival (AoA)-time of flight (ToF) with timing lock algorithm to determine its location relative to the beacons with high accuracy. The algorithm utilizes an AoA-based location method to obtain an initial estimate of its own location. Based on this, it estimates the timing offsets (TOs) between the MD clock and the beacon transmissions. The average TO and the known periodicities of the beacon signals are then used to obtain a second more accurate MD location estimate via a ToF method. The system utilizes wideband spread spectrum ultrasonic signaling in order to achieve a high update rate and robustness to noise and reverberation. A prototype system was constructed, and the algorithm was implemented in software. The experimental results show that the method provides 3-D accuracy better than 9.5 cm in 99% of cases, an 80% accuracy improvement over the conventional AoA-only method.

Journal ArticleDOI
TL;DR: A rigorous method to introduce the concept of in-loop filters and window functions into PLL systems is presented and enables smoother estimation of the signal parameters such as phase angle, frequency, and amplitude in the presence of noise and harmonics.
Abstract: This paper addresses the concept of in-loop filters in phase-locked loop (PLL) systems. The in-loop filters are derived from an optimization perspective, and an analytical method to design the controlling parameters of a PLL with in-loop filters is also presented. Such filters can also be selected as conventional window functions in which case they can be tuned to reject certain frequency components similar to the discrete Fourier transform. In this paper, a rigorous method to introduce the concept of in-loop filters and window functions into PLL systems is presented. This method enables smoother estimation of the signal parameters such as phase angle, frequency, and amplitude in the presence of noise and harmonics. The in-loop filters can be adjusted to completely remove specific harmonics. The method is first developed for a single-phase enhanced PLL system and is then extended to three-phase PLLs including the well-known synchronous-reference-frame PLL. Simulation and experimental results are also included.

Journal ArticleDOI
TL;DR: A complete wireless system for structural identification under environmental load is designed, implemented, deployed, and tested on three different real bridges, and its contribution ranges from the hardware to the graphical front end to avoid the main limitations of WNs for SHM particularly in regard to reliability, scalability, and synchronization.
Abstract: Structural health monitoring (SHM) systems have excellent potential to improve the regular operation and maintenance of structures. Wireless networks (WNs) have been used to avoid the high cost of traditional generic wired systems. The most important limitation of SHM wireless systems is time-synchronization accuracy, scalability, and reliability. A complete wireless system for structural identification under environmental load is designed, implemented, deployed, and tested on three different real bridges. Our contribution ranges from the hardware to the graphical front end. System goal is to avoid the main limitations of WNs for SHM particularly in regard to reliability, scalability, and synchronization. We reduce spatial jitter to 125 ns, far below the 120 μs required for high-precision acquisition systems and much better than the 10-μs current solutions, without adding complexity. The system is scalable to a large number of nodes to allow for dense sensor coverage of real-world structures, only limited by a compromise between measurement length and mandatory time to obtain the final result. The system addresses a myriad of problems encountered in a real deployment under difficult conditions, rather than a simulation or laboratory test bed.

Journal ArticleDOI
TL;DR: Print circuit board technology is particularly advantageous for realizing this type of sensor through fabricating the interdigitated electrode structures in the patterned Cu foil to prevent shorting in the presence of water.
Abstract: Interdigitated electrode capacitive fringing field sensors have been utilized in numerous applications. Although various technologies are used to realize these types of sensors, printed circuit board technology is particularly advantageous for realizing this type of sensor through fabricating the interdigitated electrode structures in the patterned Cu foil. Additionally, the solder mask coating can insulate the electrodes to prevent shorting in the presence of water. Using this approach, prototype sensors were designed, simulated, fabricated, and successfully evaluated. Applications include water detection and quantity measurement and soil moisture content measurement.

Journal ArticleDOI
TL;DR: In this paper, the frequency response of the zeroth- and second-order filters is established and illustrated and it is demonstrated that, for orders greater than or equal to two, the filters are able to form zero flat phase response about the operation frequency and then able to provide instantaneous estimates.
Abstract: Recently, the TaylorK Kalman filter was proposed for estimating instantaneous oscillating phasors. Its performance was examined through time-domain simulations using the benchmark test signals specified in the IEEE Standard for Synchrophasors for Power Systems. It was discovered that the estimation error level was abruptly reduced by a factor of ten from the second order, mainly because those filters were able to provide instantaneous phasor estimates. In this paper, the frequency response of the zeroth- and second-order filters is established and illustrated. They demonstrate that, for orders greater than or equal to two, the filters are able to form zero flat phase response about the operation frequency and then able to provide instantaneous estimates. By assessing the behavior of the estimates before signals with harmonics, or noise, not contemplated in the signal model, the frequency response method leads us to design more robust filters, referred to as TaylorK Kalman-Fourier, because they incorporate the whole set of harmonics in their multiharmonic signal model. It turns out that the bank of comb filters achieved with K = 0 is equivalent to that of the discrete Fourier transform, with a computational cost of one and a half products per harmonic estimate, which is lower than the FFT cost for more than eight components, and the bank of fence filters obtained with K = 2 is similar to that of the Taylor2 Fourier transform but with the advantage of providing estimates devoid of delay and needing only four products per harmonic set of estimates. Due to their instantaneous character, and computational simplicity, those estimates are certainly very useful for real-time harmonic analysis and power system control applications.

Journal ArticleDOI
TL;DR: A new tomographic algorithm that combines the logical filtered back-projection and the simultaneous algebraic reconstruction technique is proposed to reconstruct the flame sections from the images to improve the visualization and characterization of a burner flame.
Abstract: This paper presents the design, implementation, and evaluation of an optical fiber imaging based tomographic system for the 3-D visualization and characterization of a burner flame. Eight imaging fiber bundles coupled with two RGB charge-coupled device cameras are used to acquire flame images simultaneously from eight different directions around the burner. The fiber bundle has 30k picture elements and an objective lens with a 92° angle of view. The characteristic evaluation of the imaging fiber bundles and the calibration of the system were conducted to ensure the accuracy of the system. A new tomographic algorithm that combines the logical filtered back-projection and the simultaneous algebraic reconstruction technique is proposed to reconstruct the flame sections from the images. A direct comparison between the proposed algorithm and other tomographic approaches is conducted through computer simulation for different test templates and numbers of projections. The 3-D reconstruction of the cross- and longitudinal-sections of a burner flame from image projections obtained from the imaging system was also performed. The effectiveness of the imaging system and computer algorithm is assessed through experimental tests.

Journal ArticleDOI
TL;DR: In contrast to standard two-way transfer schemes which offer only comparisons of two distant clocks, the fiber-optic frequency transfer system displays distribution functionality, reproducing the time and frequency signals of the reference clock in the remote location.
Abstract: In this paper, we describe the extension of our fiber-optic frequency transfer system to the time transfer capability. In contrast to standard two-way transfer schemes which offer only comparisons of two distant clocks, our system displays distribution functionality, reproducing the time and frequency signals of the reference clock in the remote location. By using active compensation of the fiber delay fluctuations, we obtained a time deviation of 0.3 ps (for time transfer) and an Allan deviation of 1.2 × 10-17 (for frequency transfer) at 105-s averaging. The experiments presented were carried out using a 60-km-long fiber loop, forming a part of the real urban network around Krakow.

Journal ArticleDOI
TL;DR: This work collects important communication parameters to evaluate the impacts of other ISs on IEEE 802.15.4 networks and gives a rough indication of the mutual interference of the different systems when any two of the networks operate simultaneously and in range.
Abstract: With the recent emergence of standards, wireless solutions are ready to be deployed in building automation networks. IEEE 802.15.4 is a standard for short-range wireless networks. Its major application fields are home and building automation, as well as industrial sensor and actuator networks. It operates primarily in the license-free 2.4-GHz industrial, scientific, and medical band. This feature makes the technology not only easily applicable but also potentially vulnerable to interference by other technologies in this band, e.g., Bluetooth and microwave ovens. There are many possible coexistence scenarios with different network sizes, configurations, interference sources (ISs), and environmental conditions. To investigate the impacts of ISs on the performance of IEEE 802.15.4 wireless sensor networks, this paper performs several experiments with commercially available equipment. The results give a rough indication of the mutual interference of the different systems when any two of the networks operate simultaneously and in range. This work collects important communication parameters to evaluate the impacts of other ISs on IEEE 802.15.4 networks. These results should help designers better understand the challenges of building wireless applications.

Journal ArticleDOI
TL;DR: A novel inductive loop sensor that can detect vehicles under a heterogeneous and less-lane-disciplined traffic and thus can be used to support a traffic control management system in optimizing the best use of existing roads is presented.
Abstract: This paper presents a novel inductive loop sensor that can detect vehicles under a heterogeneous and less-lane-disciplined traffic and thus can be used to support a traffic control management system in optimizing the best use of existing roads. The loop sensor proposed in this paper detects large (e.g., bus) as well as small (e.g., bicycle) vehicles occupying any available space in the roadway, which is the main requirement for sensing heterogeneous and lane-less traffic. To accomplish the sensing of large as well as small vehicles, a multiple loop system with a new inductive loop sensor structure is proposed. The proposed sensor structure not only senses and segregates the vehicle type as bicycle, motor cycle, scooter, car, and bus but also enables accurate counting of the number of vehicles even in a mixed traffic flow condition. A prototype of the multiple loop sensing system has been developed and tested. Field tests indicate that the prototype successfully detected all types of vehicles and counted, correctly, the number of each type of vehicles. Thus, the suitability of the proposed sensor system for any type of traffic has been established.